For pharma teams
KAMs, MSLs, market access
- Practise on the actual journey in your market, step by step.
- Case work built on real Strategy Vectors and real stakeholders.
- Every claim you make can be traced to its source.
StrategyVectors
Patient journey databases for pharma
Know the journey.
Change the outcome.
Decision-grade patient journey databases. Every claim, every source, every piece of evidence traced and referenced.
The 12-step patient journey · scroll to follow the line
01 · The foundation
One therapeutic area. One country. Every number traced.
A Patient Journey Database maps the 12 steps patients take using 10 intelligence domains. Every data point carries a source, an evidence level, a date and its caveats.
500+A4 pages · one TA · one country
D1 Epidemiology × Step 4 Accurate DiagnosisL1 · Primary research
156new biopsy-confirmed IgAN cases per year
- Source
- Swedish Renal Registry, Annual Report 2024
- Accessed
- 2026-06-11
- Cross-check
- Rehnberg et al. 2024 · PMID 38438831
- Confidence
- High
- Caveat
- Registry captures ~60% of the true biopsy rate.
Example · IgA nephropathy · Sweden · 766 data points · 381 sources
500+ A4 pages
One therapeutic area, one country, in depth.
IgA nephropathy in Sweden: 766 data points from 381 sources.
12 steps × 10 domains
Every step of the journey, every angle of evidence.
From symptom onset to long-term outcomes; from epidemiology and pathways to market access and real-world outcomes. Six evidence levels, from L1 primary research to L6 expert estimate with reasoning and range shown.
Traced to the source
Open any claim and see where the number comes from.
Source, date, cross-check, confidence and caveat on every data point. Where there is no evidence, the database says so.
“Where there is no evidence, the database says no evidence. It never fills the gap with a guess.”How every Strategy Vectors database is built
02 · Strategy Vectors
Find the barriers that prevent patient access to treatment.
A proprietary model identifies Strategy Vectors: barriers which, if removed or reduced, may unlock patient access.
Patient flow · illustrative
Step 3 · Specialist ReferralDelayed GP-to-nephrology referral
Step 4 · Accurate DiagnosisLow biopsy rate in at-risk patients
Step 6 · Initial Treatment InterventionRestrictive reimbursement criteria
42Strategy Vectors identified
IgA nephropathy · Sweden
IgA nephropathy · Sweden
Step 3 · Specialist Referral
Delayed GP-to-nephrology referral.
Patients who should reach a specialist stay in primary care. The flow narrows before diagnosis is even possible.
Step 4 · Accurate Diagnosis
Low biopsy rate in at-risk patients.
Without a biopsy there is no confirmed diagnosis, and no treatment decision.
Step 6 · Initial Treatment Intervention
Restrictive reimbursement criteria.
Eligible patients are held back at the point of treatment. In IgAN Sweden, the model found 42 vectors like these.
03 · Vector simulation
See the future before you commit.
A causal Bayesian network runs 10 000 simulations for each Strategy Vector you choose, and shows the predicted outcome — for example, how many more patients reach treatment if the vector is removed or reduced.
Runs 0
TodaySV-31 removed · all outcomesSV-31 removed · if it works
+0more patients a year reach the treatment decision if closing the EGFR testing gap works (9–16)Chance it works: 70% · 10 000 draws
Start from today
A baseline, not a guess.
10 000 runs of the current journey give a distribution of outcomes, not a single number.
Reduce the vector
Include the chance that it fails.
The unconditional result allows for the removal not working, and shows how often that happens.
Read the difference
Unconditional and given success, side by side.
You see both, with every assumption named, before a budget is committed.
10 000 simulations per vector. Distributions, not point estimates.Causal Bayesian network · every assumption named
04 · Feasibility test
How likely is it to work, and what would it take?
For any vector: the likelihood of successful removal under different investment scenarios, plus an implementation plan built on historical precedent.
- Three investment scenariosLow, medium and high, each with a probability range rather than a single score.
- Built on precedentWhat has worked before in this healthcare system, and how long it took.
- Assumptions in the openEvery driver of the estimate is named, so your team can challenge it.
05 · Ready-to-go account plans
From a database to an account plan in days, not months.
The database maps key institutions, patient flows and stakeholders for each therapeutic area and region. Combine selected vectors with a likelihood scenario, and your KAMs have a plan that actually will make a difference.
Referring clinicians
Paediatric NF1 consultant
NF1 lead clinician
NF1 nurse specialist
Chief pharmacist
Example from a real account plan · NF1-PN · England
Foundational KAM plan · NF1-PN · England
Guy's and St Thomas' · National NF1 Centre
VectorSV-15 · Pre-treatment testing bottleneck
Echo, ECG, eye exam, liver tests and MRI run one after another before a first dose.
Echo, ECG, eye exam, liver tests and MRI run one after another before a first dose.
StrategyOne bundled work-up pathway, one coordinator, reserved scan slots, funding approval in parallel.
StakeholdersNF1 lead clinician · Chief pharmacist · NF1 nurse specialist · Cardiology, eye and radiology leads
12 months
- Initiation pathway signed off by the MDT · month 4
- One reserved slot a week for echo, OCT and MRI · month 6
- Decision to first dose under 6 weeks · month 9
Institutions and flows
See where patients enter, get stuck and drop out.
Named institution types, the patient flows between them, and where the flow is thin.
Stakeholders
The people who decide, by role.
Stakeholders per institution, with the vectors that matter to each of them.
One page
An account plan your KAMs can use tomorrow.
Selected vectors, the likelihood scenario, stakeholders and next steps for one account.
During database assembly every data point is checked as it is written. Nothing is saved without a source, an evidence level and a date.
06 · Knowledge-gap research
Every knowledge gap represents a challenge for pharma and for the healthcare system.
Every knowledge gap in the database gets singled out. Using a proprietary agentic model, we run online research studies to close those gaps, to the benefit of patients.
GAP · D2Time from first GP visit to dermatology referral
GAP · D2Share of patients who receive patch testing
GAP · D6Work days lost to flares per year
GAP · D6Out-of-pocket spend on emollients
GAP · D10Reasons for stopping first systemic therapy
GAP · D3Regional access to phototherapy
Examples · chronic hand eczema · Germany
The gap is visible
No evidence means there’s none to find.
The gap is recorded.
Every knowledge gap is named
74 named gaps in chronic hand eczema, Germany.
From a database of 2 039 data points and 566 sources.
A study bridges it
Our research protocol closes the gap.
Studies produce the missing evidence to the benefit of patients, pharma and the healthcare system.
07 · Training programmes
Train on the real world, not a textbook.
Programmes for pharma teams and for healthcare professionals, built on real-world data from the database.
For healthcare professionals
Clinicians and care teams
- See where patients are lost in your own pathway.
- Local data, set against the national picture.
Example database · IgA nephropathy · Sweden · June 2026
766data points
381sources
10domains
42Strategy Vectors
08 · Connected databases
One market teaches another.
Connect databases across countries or regions to see what differs, what converges and what one market can learn from another. Built for global brand teams and clinical development.
For example · five IgAN databases
DEFRUKITES
Same method, same grid
Five countries, one comparable structure.
Each database uses the same 12 steps and 10 domains, so a comparison is fair. For example: DE, FR, UK, IT and ES in IgA nephropathy.
What differs
Referral routes and time to treatment.
Where the strands pull apart, one market has found something another has not.
What converges
Shared barriers point to global programmes.
Where the strands meet, the barrier is common. Local differences point to local plans.
09 · External data integration
Add your own data. Open new dimensions.
Plug in external sources — for example your market research studies — to add new dimensions to the journey and sharpen decisions.
Patient journey + your data
+ segment + territory + timeYour studies, our grid
Market research and CRM map onto the same 12 steps.
Your data joins the journey where it belongs, not in a separate deck.
Provenance stays intact
Claims data and your own studies, labelled as yours.
External sources keep their origin, so every number can still be traced.
New dimensions
Segment, territory, time.
A thicker line: more ways to cut the journey, and sharper decisions.